Overview

Dataset statistics

Number of variables19
Number of observations21040
Missing cells0
Missing cells (%)0.0%
Duplicate rows195
Duplicate rows (%)0.9%
Total size in memory3.1 MiB
Average record size in memory152.0 B

Variable types

Numeric12
Categorical7

Alerts

Dataset has 195 (0.9%) duplicate rowsDuplicates
latitude is highly overall correlated with zipcodeHigh correlation
livingspace is highly overall correlated with price_displayHigh correlation
longitude is highly overall correlated with zipcodeHigh correlation
number_of_rooms is highly overall correlated with object_category and 2 other fieldsHigh correlation
object_category is highly overall correlated with number_of_rooms and 2 other fieldsHigh correlation
object_type is highly overall correlated with number_of_rooms and 1 other fieldsHigh correlation
price_display is highly overall correlated with livingspace and 2 other fieldsHigh correlation
price_display_type is highly overall correlated with price_unitHigh correlation
price_unit is highly overall correlated with price_display_typeHigh correlation
year_built is highly overall correlated with year_renovatedHigh correlation
year_renovated is highly overall correlated with year_builtHigh correlation
zipcode is highly overall correlated with latitude and 1 other fieldsHigh correlation
price_display_type is highly imbalanced (73.1%)Imbalance
price_unit is highly imbalanced (88.1%)Imbalance
is_temporary is highly imbalanced (65.5%)Imbalance
is_selling_furniture is highly imbalanced (83.9%)Imbalance
reserved is highly imbalanced (99.6%)Imbalance
year_built is highly skewed (γ1 = 145.0373963)Skewed
livingspace is highly skewed (γ1 = 118.1720306)Skewed
object_category has 11592 (55.1%) zerosZeros
number_of_rooms has 7649 (36.4%) zerosZeros
floor has 7166 (34.1%) zerosZeros
year_built has 249 (1.2%) zerosZeros
year_renovated has 380 (1.8%) zerosZeros
livingspace has 6099 (29.0%) zerosZeros

Reproduction

Analysis started2024-07-03 12:14:09.938362
Analysis finished2024-07-03 12:14:42.992154
Duration33.05 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

object_category
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2976236
Minimum0
Maximum8
Zeros11592
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:43.132830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7356588
Coefficient of variation (CV)1.1906471
Kurtosis-0.94958855
Mean2.2976236
Median Absolute Deviation (MAD)0
Skewness0.6524778
Sum48342
Variance7.4838291
MonotonicityNot monotonic
2024-07-03T14:14:43.334306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 11592
55.1%
5 3793
 
18.0%
4 3366
 
16.0%
8 1674
 
8.0%
3 342
 
1.6%
7 187
 
0.9%
2 80
 
0.4%
6 4
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
0 11592
55.1%
1 2
 
< 0.1%
2 80
 
0.4%
3 342
 
1.6%
4 3366
 
16.0%
5 3793
 
18.0%
6 4
 
< 0.1%
7 187
 
0.9%
8 1674
 
8.0%
ValueCountFrequency (%)
8 1674
 
8.0%
7 187
 
0.9%
6 4
 
< 0.1%
5 3793
 
18.0%
4 3366
 
16.0%
3 342
 
1.6%
2 80
 
0.4%
1 2
 
< 0.1%
0 11592
55.1%

object_type
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.403612
Minimum0
Maximum58
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:43.568650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median16
Q337
95-th percentile48
Maximum58
Range58
Interquartile range (IQR)35

Descriptive statistics

Standard deviation18.134931
Coefficient of variation (CV)0.93461625
Kurtosis-1.3785348
Mean19.403612
Median Absolute Deviation (MAD)14
Skewness0.40451101
Sum408252
Variance328.87573
MonotonicityNot monotonic
2024-07-03T14:14:43.811315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9501
45.2%
27 2267
 
10.8%
37 1735
 
8.2%
47 1674
 
8.0%
25 1127
 
5.4%
39 996
 
4.7%
14 620
 
2.9%
53 421
 
2.0%
16 360
 
1.7%
21 308
 
1.5%
Other values (49) 2031
 
9.7%
ValueCountFrequency (%)
0 27
 
0.1%
1 2
 
< 0.1%
2 9501
45.2%
3 12
 
0.1%
4 39
 
0.2%
5 7
 
< 0.1%
6 3
 
< 0.1%
7 225
 
1.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
58 13
 
0.1%
57 21
 
0.1%
56 4
 
< 0.1%
55 20
 
0.1%
54 127
 
0.6%
53 421
2.0%
52 1
 
< 0.1%
51 40
 
0.2%
50 249
1.2%
49 105
 
0.5%

price_display
Real number (ℝ)

HIGH CORRELATION 

Distinct2799
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.34
Minimum1
Maximum62539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:44.049360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile80.95
Q1400
median1460
Q32140
95-th percentile3820.25
Maximum62539
Range62538
Interquartile range (IQR)1740

Descriptive statistics

Standard deviation1543.4116
Coefficient of variation (CV)0.97539823
Kurtosis142.98552
Mean1582.34
Median Absolute Deviation (MAD)810
Skewness6.0743452
Sum33292433
Variance2382119.4
MonotonicityNot monotonic
2024-07-03T14:14:44.291087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 452
 
2.1%
130 380
 
1.8%
150 367
 
1.7%
100 256
 
1.2%
50 220
 
1.0%
140 218
 
1.0%
110 187
 
0.9%
1500 168
 
0.8%
250 140
 
0.7%
60 140
 
0.7%
Other values (2789) 18512
88.0%
ValueCountFrequency (%)
1 6
 
< 0.1%
10 9
 
< 0.1%
15 6
 
< 0.1%
20 58
0.3%
21 1
 
< 0.1%
22 1
 
< 0.1%
25 32
0.2%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
62539 1
< 0.1%
30000 1
< 0.1%
27155 1
< 0.1%
25000 1
< 0.1%
22665 1
< 0.1%
22000 1
< 0.1%
20500 1
< 0.1%
20000 1
< 0.1%
19390 1
< 0.1%
18493 1
< 0.1%

price_display_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.5 KiB
1
20071 
0
 
969

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 20071
95.4%
0 969
 
4.6%

Length

2024-07-03T14:14:44.507982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:14:44.679142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 20071
95.4%
0 969
 
4.6%

Most occurring characters

ValueCountFrequency (%)
1 20071
95.4%
0 969
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20071
95.4%
0 969
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20071
95.4%
0 969
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20071
95.4%
0 969
 
4.6%

price_unit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.5 KiB
1
20061 
4
 
969
0
 
5
3
 
4
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21040
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 20061
95.3%
4 969
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Length

2024-07-03T14:14:44.854136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:14:45.039215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 20061
95.3%
4 969
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 20061
95.3%
4 969
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20061
95.3%
4 969
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20061
95.3%
4 969
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20061
95.3%
4 969
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

number_of_rooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9306321
Minimum0
Maximum10.5
Zeros7649
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:45.232723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q33.5
95-th percentile4.5
Maximum10.5
Range10.5
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation1.8400371
Coefficient of variation (CV)0.95307492
Kurtosis-0.80014508
Mean1.9306321
Median Absolute Deviation (MAD)1.5
Skewness0.46796947
Sum40620.5
Variance3.3857364
MonotonicityNot monotonic
2024-07-03T14:14:45.431700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 7649
36.4%
3.5 2989
 
14.2%
1 2448
 
11.6%
4.5 2113
 
10.0%
2.5 1978
 
9.4%
3 1031
 
4.9%
2 730
 
3.5%
4 626
 
3.0%
1.5 556
 
2.6%
5.5 528
 
2.5%
Other values (10) 392
 
1.9%
ValueCountFrequency (%)
0 7649
36.4%
1 2448
 
11.6%
1.5 556
 
2.6%
2 730
 
3.5%
2.5 1978
 
9.4%
3 1031
 
4.9%
3.5 2989
 
14.2%
4 626
 
3.0%
4.5 2113
 
10.0%
5 127
 
0.6%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
10 7
 
< 0.1%
9 5
 
< 0.1%
8.5 12
 
0.1%
8 13
 
0.1%
7.5 37
 
0.2%
7 24
 
0.1%
6.5 109
 
0.5%
6 57
 
0.3%
5.5 528
2.5%

floor
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2978137
Minimum-8
Maximum31
Zeros7166
Zeros (%)34.1%
Negative2201
Negative (%)10.5%
Memory size164.5 KiB
2024-07-03T14:14:45.632495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile-1
Q10
median1
Q32
95-th percentile5
Maximum31
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1021933
Coefficient of variation (CV)1.6197959
Kurtosis14.851073
Mean1.2978137
Median Absolute Deviation (MAD)1
Skewness2.4243785
Sum27306
Variance4.4192167
MonotonicityNot monotonic
2024-07-03T14:14:45.876775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 7166
34.1%
1 3664
17.4%
2 3360
16.0%
3 2290
 
10.9%
-1 1792
 
8.5%
4 1154
 
5.5%
5 571
 
2.7%
-2 292
 
1.4%
6 254
 
1.2%
7 113
 
0.5%
Other values (23) 384
 
1.8%
ValueCountFrequency (%)
-8 1
 
< 0.1%
-6 2
 
< 0.1%
-5 6
 
< 0.1%
-4 35
 
0.2%
-3 73
 
0.3%
-2 292
 
1.4%
-1 1792
 
8.5%
0 7166
34.1%
1 3664
17.4%
2 3360
16.0%
ValueCountFrequency (%)
31 1
 
< 0.1%
25 1
 
< 0.1%
24 3
 
< 0.1%
23 3
 
< 0.1%
22 2
 
< 0.1%
21 2
 
< 0.1%
19 3
 
< 0.1%
18 7
< 0.1%
17 4
< 0.1%
16 8
< 0.1%

is_furnished
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.5 KiB
0.0
18067 
1.0
2973 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63120
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18067
85.9%
1.0 2973
 
14.1%

Length

2024-07-03T14:14:46.091830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:14:46.262127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18067
85.9%
1.0 2973
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 39107
62.0%
. 21040
33.3%
1 2973
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39107
62.0%
. 21040
33.3%
1 2973
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39107
62.0%
. 21040
33.3%
1 2973
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39107
62.0%
. 21040
33.3%
1 2973
 
4.7%

is_temporary
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.5 KiB
0.0
19685 
1.0
 
1355

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63120
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19685
93.6%
1.0 1355
 
6.4%

Length

2024-07-03T14:14:46.437735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:14:46.606907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19685
93.6%
1.0 1355
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 40725
64.5%
. 21040
33.3%
1 1355
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 40725
64.5%
. 21040
33.3%
1 1355
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 40725
64.5%
. 21040
33.3%
1 1355
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 40725
64.5%
. 21040
33.3%
1 1355
 
2.1%

is_selling_furniture
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.5 KiB
0.0
20545 
1.0
 
495

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63120
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 20545
97.6%
1.0 495
 
2.4%

Length

2024-07-03T14:14:46.781116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:14:46.949058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 20545
97.6%
1.0 495
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 41585
65.9%
. 21040
33.3%
1 495
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 41585
65.9%
. 21040
33.3%
1 495
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 41585
65.9%
. 21040
33.3%
1 495
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 41585
65.9%
. 21040
33.3%
1 495
 
0.8%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct1758
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5562.6028
Minimum1000
Maximum9657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:47.147546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1163
Q13095
median5430
Q38152
95-th percentile9030
Maximum9657
Range8657
Interquartile range (IQR)5057

Descriptive statistics

Standard deviation2746.2653
Coefficient of variation (CV)0.49370149
Kurtosis-1.4041526
Mean5562.6028
Median Absolute Deviation (MAD)2620
Skewness-0.16624892
Sum1.1703716 × 108
Variance7541972.9
MonotonicityNot monotonic
2024-07-03T14:14:47.386268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 438
 
2.1%
1700 325
 
1.5%
8050 280
 
1.3%
2300 250
 
1.2%
8004 245
 
1.2%
8048 182
 
0.9%
4052 180
 
0.9%
8400 179
 
0.9%
4123 159
 
0.8%
8003 155
 
0.7%
Other values (1748) 18647
88.6%
ValueCountFrequency (%)
1000 6
 
< 0.1%
1001 1
 
< 0.1%
1002 2
 
< 0.1%
1003 86
0.4%
1004 84
0.4%
1005 31
 
0.1%
1006 45
0.2%
1007 70
0.3%
1008 50
0.2%
1009 43
0.2%
ValueCountFrequency (%)
9657 3
 
< 0.1%
9656 4
 
< 0.1%
9650 5
 
< 0.1%
9643 2
 
< 0.1%
9642 1
 
< 0.1%
9630 19
0.1%
9620 5
 
< 0.1%
9615 2
 
< 0.1%
9607 2
 
< 0.1%
9606 11
0.1%

city
Real number (ℝ)

Distinct2176
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1129.7682
Minimum0
Maximum2175
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:47.627465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile128
Q1505
median1110
Q31747
95-th percentile2083
Maximum2175
Range2175
Interquartile range (IQR)1242

Descriptive statistics

Standard deviation689.04751
Coefficient of variation (CV)0.6099017
Kurtosis-1.347861
Mean1129.7682
Median Absolute Deviation (MAD)613
Skewness-0.056871878
Sum23770322
Variance474786.48
MonotonicityNot monotonic
2024-07-03T14:14:47.868174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2083 2005
 
9.5%
147 962
 
4.6%
1696 639
 
3.0%
180 547
 
2.6%
2072 470
 
2.2%
984 454
 
2.2%
2010 360
 
1.7%
650 324
 
1.5%
681 269
 
1.3%
940 247
 
1.2%
Other values (2166) 14763
70.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 7
 
< 0.1%
5 93
0.4%
6 19
 
0.1%
7 4
 
< 0.1%
8 27
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2175 1
 
< 0.1%
2174 1
 
< 0.1%
2173 11
0.1%
2172 16
0.1%
2171 1
 
< 0.1%
2170 1
 
< 0.1%
2169 1
 
< 0.1%
2168 1
 
< 0.1%
2167 1
 
< 0.1%
2166 1
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct16405
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.159366
Minimum45.826182
Maximum47.793652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:48.105084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum45.826182
5-th percentile46.214642
Q146.957582
median47.344902
Q347.430402
95-th percentile47.559892
Maximum47.793652
Range1.96747
Interquartile range (IQR)0.47282

Descriptive statistics

Standard deviation0.40260647
Coefficient of variation (CV)0.0085371477
Kurtosis0.7002692
Mean47.159366
Median Absolute Deviation (MAD)0.169395
Skewness-1.2432827
Sum992233.06
Variance0.16209197
MonotonicityNot monotonic
2024-07-03T14:14:48.361696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.77822161 27
 
0.1%
47.11767161 24
 
0.1%
46.0127616 23
 
0.1%
47.43064161 22
 
0.1%
47.4241816 21
 
0.1%
47.1131916 20
 
0.1%
46.23478161 18
 
0.1%
47.21403161 15
 
0.1%
47.1582316 14
 
0.1%
47.44245161 14
 
0.1%
Other values (16395) 20842
99.1%
ValueCountFrequency (%)
45.8261816 1
 
< 0.1%
45.8310216 1
 
< 0.1%
45.8322416 3
< 0.1%
45.8329516 2
< 0.1%
45.8332216 1
 
< 0.1%
45.83389161 2
< 0.1%
45.8353816 1
 
< 0.1%
45.8357516 1
 
< 0.1%
45.8379816 1
 
< 0.1%
45.83800161 1
 
< 0.1%
ValueCountFrequency (%)
47.79365161 1
< 0.1%
47.7680316 1
< 0.1%
47.7566216 1
< 0.1%
47.75052161 1
< 0.1%
47.75009161 1
< 0.1%
47.75003161 1
< 0.1%
47.7495116 1
< 0.1%
47.7469616 1
< 0.1%
47.74692161 1
< 0.1%
47.74690161 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct16748
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0237921
Minimum5.9918812
Maximum10.364311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:48.623014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.9918812
5-th percentile6.5714497
Q17.4626812
median8.2036562
Q38.5886087
95-th percentile9.3810212
Maximum10.364311
Range4.37243
Interquartile range (IQR)1.1259275

Descriptive statistics

Standard deviation0.84856367
Coefficient of variation (CV)0.10575594
Kurtosis-0.64354106
Mean8.0237921
Median Absolute Deviation (MAD)0.609005
Skewness-0.28147813
Sum168820.58
Variance0.7200603
MonotonicityNot monotonic
2024-07-03T14:14:48.862285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.840561235 37
 
0.2%
7.156761234 27
 
0.1%
8.96301124 23
 
0.1%
9.383271235 22
 
0.1%
8.558061235 21
 
0.1%
7.301061235 20
 
0.1%
6.124081239 18
 
0.1%
7.785771235 15
 
0.1%
9.392591235 13
 
0.1%
8.521711235 13
 
0.1%
Other values (16738) 20831
99.0%
ValueCountFrequency (%)
5.991881235 1
< 0.1%
5.993681235 1
< 0.1%
6.019591235 1
< 0.1%
6.019631235 1
< 0.1%
6.036691235 1
< 0.1%
6.049101235 1
< 0.1%
6.061721235 1
< 0.1%
6.062851235 1
< 0.1%
6.066581235 1
< 0.1%
6.067841235 1
< 0.1%
ValueCountFrequency (%)
10.36431123 1
< 0.1%
10.29604123 1
< 0.1%
10.09659123 1
< 0.1%
9.880551235 1
< 0.1%
9.875381235 1
< 0.1%
9.868861235 1
< 0.1%
9.833921236 1
< 0.1%
9.831621235 1
< 0.1%
9.824991235 1
< 0.1%
9.824821235 1
< 0.1%

year_built
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct235
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4163.9292
Minimum0
Maximum19702024
Zeros249
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:49.102146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1943
Q11998
median4111.237
Q34111.237
95-th percentile4111.237
Maximum19702024
Range19702024
Interquartile range (IQR)2113.237

Descriptive statistics

Standard deviation135809.8
Coefficient of variation (CV)32.61578
Kurtosis21037.231
Mean4163.9292
Median Absolute Deviation (MAD)0
Skewness145.0374
Sum87609070
Variance1.8444302 × 1010
MonotonicityNot monotonic
2024-07-03T14:14:49.361980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4111.23695 12536
59.6%
2024 365
 
1.7%
0 249
 
1.2%
2023 239
 
1.1%
2022 188
 
0.9%
2018 184
 
0.9%
2017 176
 
0.8%
1970 167
 
0.8%
2019 163
 
0.8%
2015 163
 
0.8%
Other values (225) 6610
31.4%
ValueCountFrequency (%)
0 249
1.2%
6 2
 
< 0.1%
177 1
 
< 0.1%
199 1
 
< 0.1%
1250 1
 
< 0.1%
1255 1
 
< 0.1%
1300 1
 
< 0.1%
1350 1
 
< 0.1%
1369 1
 
< 0.1%
1400 3
 
< 0.1%
ValueCountFrequency (%)
19702024 1
 
< 0.1%
20220 1
 
< 0.1%
4111.23695 12536
59.6%
2997 1
 
< 0.1%
2026 9
 
< 0.1%
2025 32
 
0.2%
2024 365
 
1.7%
2023 239
 
1.1%
2022 188
 
0.9%
2021 157
 
0.7%

year_renovated
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct171
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3113.0409
Minimum0
Maximum20220
Zeros380
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:49.622221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1962
Q12013
median4111.237
Q34111.237
95-th percentile4111.237
Maximum20220
Range20220
Interquartile range (IQR)2098.237

Descriptive statistics

Standard deviation1128.8373
Coefficient of variation (CV)0.36261563
Kurtosis1.3081468
Mean3113.0409
Median Absolute Deviation (MAD)0
Skewness-0.26025361
Sum65498380
Variance1274273.6
MonotonicityNot monotonic
2024-07-03T14:14:49.888054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4111.23695 11442
54.4%
2024 910
 
4.3%
2023 599
 
2.8%
2022 438
 
2.1%
0 380
 
1.8%
2021 344
 
1.6%
2020 329
 
1.6%
2018 326
 
1.5%
2017 321
 
1.5%
2019 316
 
1.5%
Other values (161) 5635
26.8%
ValueCountFrequency (%)
0 380
1.8%
6 2
 
< 0.1%
24 1
 
< 0.1%
1250 1
 
< 0.1%
1255 1
 
< 0.1%
1400 1
 
< 0.1%
1403 2
 
< 0.1%
1437 2
 
< 0.1%
1479 1
 
< 0.1%
1491 1
 
< 0.1%
ValueCountFrequency (%)
20220 1
 
< 0.1%
4111.23695 11442
54.4%
2027 1
 
< 0.1%
2026 9
 
< 0.1%
2025 40
 
0.2%
2024 910
 
4.3%
2023 599
 
2.8%
2022 438
 
2.1%
2021 344
 
1.6%
2020 329
 
1.6%

moving_date_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.5 KiB
1
7447 
2
7401 
0
6192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21040
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 7447
35.4%
2 7401
35.2%
0 6192
29.4%

Length

2024-07-03T14:14:50.117430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:14:50.294675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 7447
35.4%
2 7401
35.2%
0 6192
29.4%

Most occurring characters

ValueCountFrequency (%)
1 7447
35.4%
2 7401
35.2%
0 6192
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7447
35.4%
2 7401
35.2%
0 6192
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7447
35.4%
2 7401
35.2%
0 6192
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7447
35.4%
2 7401
35.2%
0 6192
29.4%

reserved
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.5 KiB
0.0
21033 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63120
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 21033
> 99.9%
1.0 7
 
< 0.1%

Length

2024-07-03T14:14:50.482009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:14:50.656065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 21033
> 99.9%
1.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 42073
66.7%
. 21040
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 42073
66.7%
. 21040
33.3%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 42073
66.7%
. 21040
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 42073
66.7%
. 21040
33.3%
1 7
 
< 0.1%

livingspace
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct607
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.67847
Minimum0
Maximum90000
Zeros6099
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size164.5 KiB
2024-07-03T14:14:50.859013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median58
Q394
95-th percentile196
Maximum90000
Range90000
Interquartile range (IQR)94

Descriptive statistics

Standard deviation668.49308
Coefficient of variation (CV)8.2858919
Kurtosis15633.205
Mean80.67847
Median Absolute Deviation (MAD)46
Skewness118.17203
Sum1697475
Variance446883
MonotonicityNot monotonic
2024-07-03T14:14:51.097513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6099
29.0%
70 375
 
1.8%
80 343
 
1.6%
100 325
 
1.5%
90 294
 
1.4%
75 280
 
1.3%
60 264
 
1.3%
65 255
 
1.2%
85 239
 
1.1%
12 237
 
1.1%
Other values (597) 12329
58.6%
ValueCountFrequency (%)
0 6099
29.0%
1 16
 
0.1%
2 27
 
0.1%
3 10
 
< 0.1%
4 8
 
< 0.1%
5 4
 
< 0.1%
6 14
 
0.1%
7 16
 
0.1%
8 28
 
0.1%
9 21
 
0.1%
ValueCountFrequency (%)
90000 1
< 0.1%
23000 1
< 0.1%
11000 1
< 0.1%
8033 1
< 0.1%
8000 1
< 0.1%
6000 1
< 0.1%
5443 2
< 0.1%
3934 1
< 0.1%
3892 1
< 0.1%
3773 1
< 0.1%

Interactions

2024-07-03T14:14:39.337828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:12.165847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:14.521079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:17.514018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:20.069685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:22.574752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:25.063602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:27.386066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:29.679286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:32.103564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:34.364794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:37.021079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:39.527806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:12.371758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:14.726753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:17.710995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:20.340755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:22.771235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:25.257838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:27.631447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:29.894876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:32.301695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:34.581681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:37.222965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:39.707982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:12.565655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:14.936486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:17.903860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:20.546988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:22.965459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:25.439827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:27.823773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:30.091716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:32.479680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:34.787120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:37.415079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:39.884968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:12.756909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:15.131584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:18.114218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:20.744792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:23.152349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:25.622506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:28.053147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:30.291327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:32.657759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:34.987008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:37.603033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:40.055320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:12.945590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:15.309228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:18.313781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:21.030396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:23.332553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:25.804367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:28.236652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:30.483269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:32.833708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:35.243342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:37.791139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:40.238312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:13.143077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:15.503641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:18.543466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:21.279295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:23.521009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:25.995618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:28.422273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:30.693151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:33.024241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:35.526581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:37.990135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:40.400366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:13.345072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:15.686114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:18.728729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:21.465528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:23.715211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:26.162643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:28.589317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:30.880861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:33.198711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:35.724157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:38.172318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:40.567724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:13.527204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:15.866264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:18.901372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:21.644702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:23.892749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:26.332064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:28.753194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:31.071338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:33.370370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:35.912187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:38.346629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:41.441012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:13.735587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:16.071806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:19.117723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:21.839523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:24.111013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:26.532906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:28.951130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:31.286326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:33.570747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:36.166805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:38.560168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:41.612175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:13.912447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:16.249131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:19.295482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:22.010178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:24.378316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:26.697299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:29.122136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:31.479466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:33.743237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:36.392954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:38.745411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:41.807325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:14.124924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:16.450263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:19.522364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:22.206059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:24.657652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:26.914391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:29.318191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:31.704097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:33.947021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:36.623214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:38.954552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:41.999169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:14.338517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:16.667452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:19.838957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:22.404084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:24.878080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:27.175697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:29.509557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:31.918050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:34.198007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:36.832250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:14:39.156977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-03T14:14:51.279998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
cityflooris_furnishedis_selling_furnitureis_temporarylatitudelivingspacelongitudemoving_date_typenumber_of_roomsobject_categoryobject_typeprice_displayprice_display_typeprice_unitreservedyear_builtyear_renovatedzipcode
city1.000-0.0130.1880.0610.1690.048-0.0590.2960.060-0.0470.0680.0580.0370.0270.0110.0000.0540.0520.307
floor-0.0131.0000.1280.0480.0700.0010.335-0.0300.1310.468-0.462-0.3430.4070.0220.0000.0000.0090.012-0.044
is_furnished0.1880.1281.0000.0280.4810.026-0.0360.1140.1730.0540.0030.1250.1570.0800.0820.0000.1720.1740.102
is_selling_furniture0.0610.0480.0281.0000.0070.0090.0070.0350.0690.036-0.010-0.0310.0350.0140.0440.0000.0550.0410.036
is_temporary0.1690.0700.4810.0071.0000.0390.0150.0770.1550.0610.0070.0370.0700.0270.0280.0000.1350.1370.084
latitude0.0480.0010.0260.0090.0391.000-0.0090.4580.079-0.0190.0450.019-0.0440.0250.0170.000-0.014-0.0180.540
livingspace-0.0590.335-0.0360.0070.015-0.0091.000-0.0280.0050.455-0.419-0.2290.6030.0530.0280.000-0.120-0.113-0.038
longitude0.296-0.0300.1140.0350.0770.458-0.0281.0000.080-0.0380.0800.075-0.0360.0360.0150.0000.0090.0100.945
moving_date_type0.0600.1310.1730.0690.1550.0790.0050.0801.000-0.0400.0790.020-0.1380.1220.0860.0170.0070.004-0.029
number_of_rooms-0.0470.4680.0540.0360.061-0.0190.455-0.038-0.0401.000-0.747-0.6310.6340.2270.1130.006-0.065-0.065-0.046
object_category0.068-0.4620.003-0.0100.0070.045-0.4190.0800.079-0.7471.0000.795-0.6500.4930.2470.0000.0600.0620.103
object_type0.058-0.3430.125-0.0310.0370.019-0.2290.0750.020-0.6310.7951.000-0.4050.3430.1740.0000.1050.1140.088
price_display0.0370.4070.1570.0350.070-0.0440.603-0.036-0.1380.634-0.650-0.4051.0000.0180.0000.0000.0010.006-0.044
price_display_type0.0270.0220.0800.0140.0270.0250.0530.0360.1220.2270.4930.3430.0181.0001.0000.0000.0450.047-0.022
price_unit0.0110.0000.0820.0440.0280.0170.0280.0150.0860.1130.2470.1740.0001.0001.0000.000-0.045-0.0460.023
reserved0.0000.0000.0000.0000.0000.0000.0000.0000.0170.0060.0000.0000.0000.0000.0001.000-0.006-0.004-0.005
year_built0.0540.0090.1720.0550.135-0.014-0.1200.0090.007-0.0650.0600.1050.0010.045-0.045-0.0061.0000.9110.008
year_renovated0.0520.0120.1740.0410.137-0.018-0.1130.0100.004-0.0650.0620.1140.0060.047-0.046-0.0040.9111.0000.007
zipcode0.307-0.0440.1020.0360.0840.540-0.0380.945-0.029-0.0460.1030.088-0.044-0.0220.023-0.0050.0080.0071.000

Missing values

2024-07-03T14:14:42.261641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-03T14:14:42.737977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace
0527130.0110.00.00.00.00.05600101347.3847538.1827504111.236954111.2369500.00.0
1442325.0110.00.00.00.00.0860050447.3978868.6008502018.000002018.0000020.0119.0
2051610.0111.00.01.00.00.09008169647.4424309.3925104111.236954111.2369510.017.0
38471350.0111.05.01.01.00.08005208347.3893038.5132002004.000002004.0000010.018.0
4022370.0112.50.00.00.00.02000120346.7988036.8550102004.000002004.0000020.0145.0
5441110.0110.00.00.00.00.0828092147.6519839.1703874111.236954111.2369520.00.0
653940.0110.00.00.00.00.04208124947.3935007.6145624111.236954111.2369520.00.0
744195.0110.00.00.00.00.04632180547.3701607.9146664111.236954111.2369520.00.0
852795.0110.00.00.00.00.0422625347.4022917.5443794111.236954111.2369520.00.0
953960.0110.00.00.00.00.0501857047.3979018.0165294111.236954111.2369500.00.0
object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace
210308471100.0111.02.00.01.00.08055208347.3718928.5113514111.236954111.2369510.00.0
21031527130.0110.00.00.00.00.08604191347.4061228.6811114111.236954111.2369520.00.0
210328471137.0111.01.00.00.00.08004208347.3808028.5173211850.000002010.0000020.0105.0
21033023270.0113.52.01.00.00.08048207247.3848728.4936512018.000002018.0000010.0102.0
210348471390.0111.00.00.00.00.08002208347.3644728.5336214111.236954111.2369520.00.0
210358471290.0111.01.01.00.00.08006208347.3899728.5375911930.000002019.0000020.016.0
21036022500.0114.01.00.00.00.0830291347.4448728.5811411960.000002023.0000010.074.0
21037847600.0111.00.01.00.00.0250320347.1289427.2605411942.000002008.0000000.060.0
21038021125.0111.01.00.00.00.0306523546.9736627.4893312024.000002024.0000000.027.0
21039022050.0113.52.00.00.00.0895348147.4058128.4034514111.236954111.2369510.092.0

Duplicate rows

Most frequently occurring

object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace# duplicates
140527250.0110.0-2.00.00.00.0121899046.2347826.1240812012.000002012.0000020.00.014
87527100.0110.0-1.00.00.00.08718161647.1582329.0728911989.000001989.0000020.01.07
136527230.0110.0-2.00.00.00.0120368146.2050326.1279512005.000002005.0000020.00.07
7751640.0110.00.00.00.00.0102236846.5330626.5804711965.000002007.0000020.00.06
7851640.0110.00.00.00.00.0102236846.5337326.5807011961.000002003.0000020.00.06
88527100.0110.0-1.00.00.00.08718161647.1582329.0728911989.000001989.0000020.014.06
15553985.0110.00.00.00.00.01400204546.7685626.6414711973.000002010.0000020.011.05
163539110.0110.00.00.00.00.0230094047.0914426.8122814111.236954111.2369520.00.05
69453250.0110.0-1.00.00.00.0922022747.4978829.2408010.000000.0000020.06.04
141527250.0110.0-1.00.00.00.0121899046.2347826.1240812012.000002012.0000020.00.04